重庆市黔江区降水地球化学特征

为了解生态旅游城市重庆市黔江区大气污染状况,2015年采集了91个降水样品,分析了降水中离子组分分布特征,运用富集因子法、海盐示踪法、相关性分析、主成分分析、聚类分析和HYSPLIT模型分析了降水化学组分来源。研究结果表明:黔江区域降水p H为5.66~6.96,加权平均值为6.34,降水离子组分浓度大小次序为SO_4~(2-)Ca~(2+)NH_4~+Mg~(2+)NO_3~-Cl~-Na~+K~+F~-,SO_4~(2-)、Ca~(2+)之和占总离子的63.95%;除Mg~(2+)和K+外,其余组分离子浓度与总离子浓度随季节变化(冬季春季秋季夏季)呈同样的变化特征。Ca~(2+)、Mg~(2+)和K+大部分均来源于陆源贡献,Na~+可能受到了海洋源的影响,SO_4~(2-)和NO_3~-主要来源于人为输入源的贡献,Cl~-是受土壤物质和海洋的双重影响。轨迹水汽运输结果表明:黔江区域的降水主要受到西北气团、西南季风、西风环流和极地气候共同作用输入。降水中各个离子组分均表现出显著性或极显著性关系,主成分分析结果表明,第一主成分上研究的降水离子组分中都具有相对较大正载荷,第二组分pH、降水量和气温为一类。     

淮南市降水化学组成特征与来源分析

2013—2014年,逐次采集淮南城市大气降水样品,对其离子化学组分进行分析测试,并利用酸度分析、中和因子和富集系数等方法对其酸碱物质平衡和离子来源进行了分析。化学组分分析结果表明,淮南城市降水pH为6.23~7.03,雨量加权平均值为6.68,整体上降水没有呈现酸化,大部分酸性物质能被碱性物质中和。主要阴离子为SO_4~(2-)、NO_3~-,雨量加权平均值分别为147.02、62.16μeq/L,两者分别占阴离子总浓度的60.3%、25.3%;主要阳离子为Ca~(2+)、NH_4~+,雨量加权平均值分别为126.42、96.43μeq/L,分别占阳离子总浓度的44.9%、34.3%。利用富集系数法计算结果表明,SO_4~(2-)、NO_3~-主要来源于人为活动排放,Cl~-主要为海洋输入,而Ca~(2+)、Mg~(2+)、K~+则主要来自陆源输入和人为活动。     

奎独乌区域大气降水离子来源分析

通过研究2010—2015年奎独乌区域大气降水化学特征和变化,分析降水中离子组成和来源。结果表明,2010—2015年奎独乌区域硫酸型大气污染较为明显,硫酸盐污染物对降水酸性影响略降,土壤扬尘带来的钙离子污染略降;降水中SO_4~(2-)、Cl~-、Ca~(2+)、Na~+、F~-、K~+主要来源于土壤扬尘,其中SO_4~(2-)、Cl-、Ca~(2+)还来源于人为源排放;NO-3主要来源于人为源排放。工业源SO_2、烟粉尘排放量变化是引起降水中SO_4~(2-)、Ca~(2+)当量浓度变化的主要因素,大风和扬尘天气的减少也是降水中Ca~(2+)当量浓度降低的重要因素;工业源和机动车NO_x排放量变化是引起降水中NO_3~-当量浓度变化的主要因素。     

2010—2019年台州市市区降水化学组成特征及变化趋势

为了解台州市市区大气降水化学成分组成特征及变化规律,对2010—2019年台州市市区降水监测数据进行了统计分析。结果表明:2010—2019年降水样品pH为4.20～4.84夏高冬低,强酸性降水频率下降显著,电导率平均值为3.16 mS/cm。SO_4~(2-)和NO_3~-是降水中最主要的阴离子,NH_4~+和Ca~(2+)是降水中最主要的阳离子。Ca~(2+)浓度在2018年开始有所抬升,SO_4~(2-)和NO_3~-浓度整体呈波动下降趋势。SO_4~(2-)与NO_3~-浓度比均值为1.50,呈下降趋势,同大气中SO_2与NO_2的质量浓度比变化趋势基本一致。SO_4~(2-)和NO_3~-相关性显著,Cl~-、Na~+及Mg~(2+)三者之间具有较好相关性。降水与气态污染物相关性不大,对颗粒物有明显冲刷去除作用。SO_2和NO_x的排放量显著下降,酸雨污染呈现改善过程。     

“奎屯—独山子”区域秋冬季大气PM_(2.5)中水溶性离子特征分析

通过手工采集"奎屯—独山子"区域中冬季PM_(2.5),对SO_4~(2-)、NO_3~-、NO_2~-、Cl~-、F~-、K~+、Ca~(2+)、Na~+、Mg~(2+)和NH_4~+等水溶性离子特征进行了研究,结果表明:"奎屯—独山子"区域冬季PM_(2.5)中水溶性无机离子浓度分布为工业区与生活区较为相近,其中F~-、NH_4~+和K~+在2#生活区的浓度最大;NO_3~(2-)、Na~+、Ca~(2+)、Mg~(2+)在1#生活区的浓度最大;SO_4~(2-)在1#生活区和4#工业园区的浓度最大;Cl~-在3#工业园区的浓度最大。通过与北京同期数据相比,除SO_4~(2+)外,其余离子浓度与北京同期相当或低于北京同期值;"奎屯—独山子"区域冬季颗粒物整体呈酸性,生活区2#点酸性最弱,1#点最强,生活区和工业区无明显功能区分布特征;本次调查期间属于重污染天气,且本次污染期间固定源较之于移动源的贡献更显著。     

用氯仿稳定降水成份的问题

降水中加入CHCl_3、对降水中的pH值、有机酸和醛起到稳定作用。对无机离子K~+、Na~+、NH_4~+、Ca~(2+)、Mg~(2+)、NO_3~-、SO_4~(2-),加与不加CHCl_3,测得的浓度无显著差别。CHCl_3的加入可引入少量F~-和Cl~-,降水的实际浓度应减去此本底值。     

北京市典型重污染过程中PM2.5载带水溶性无机离子污染特征分析

为研究北京地区冬季PM_(2.5)载带的水溶性无机离子组分污染特征,2013年1月在中国环境科学研究院内采用在线离子色谱(URG-9000B,AIM-IC)对PM_(2.5)中水溶性无机离子(SO_4~(2-)、NO_3~-、Cl~-、NH_4~+、Na~+、K~+、Mg~(2+)、Ca~(2+))进行监测与分析。结果表明,采样期间总水溶性无机离子(TWSI)浓度为61.0μg/m~3,其中二次无机离子SO_4~(2-)、NO_3~-、NH_4~+(SNA)占比达72.3%,在PM_(2.5)中占比为40.29%,表明北京市PM_(2.5)二次污染严重。重污染天[NO_3~-]/[SO_4~(2-)]表明,固定源污染较移动源更为显著。三元相图表明,在空气质量为优的情况下,NH_4~+(在SNA中占比为30.3%~65.5%,下同)主要以NH_4NO_3的形式存在,较少比例以(NH_4)_2SO_4存在;严重污染时,NH_4~+(47.3%~77.9%)主要以(NH_4)_2SO_4形式存在,其次以NH_4NO_3的形式存在,其余的NH_4~+以NH_4Cl的形式存在。[NO_3~-]/[SO_4~(2-)]日变化表明,早、晚机动车高峰影响北京重污染发生。     

镇江市东部片区大气细颗粒物中水溶性离子在线监测成分特征研究

2019年1-6月之间利用MARGA离子在线分析仪在镇江东部地区对细颗粒物中主要水溶性离子组分NH_4~+,Na~+,K~+,Ca~(2+),Mg~(2+),Cl-,NO_3~-,SO_4~(2-)的在线监测,结果表明:细颗粒物中阴阳离子的当量浓度的频数分布趋势基本一致。通过相关性系数分析,NO_3~-和SO_4~(2-)存在显著相关性,表明与燃料燃烧有一定关系。Na~+和K~+存在显著相关性,生物质燃烧可能为其共同来源。Mg~(2+)和Ca~(2+)相关性也较强,表明可能来自于土壤扬尘。整体上镇江市东部地区点位受扬尘影响较小,受工业排放机动车尾气和燃料燃烧影响较大,说明因加强生产企业节能减排、超低排放,汽柴油车和燃料燃烧的管理。     

连云港春季PM2.5中主要水溶性无机离子污染特征及来源解析

于2017年3月1日—5月31日监测分析了连云港市大气PM_(2.5)中主要水溶性无机离子质量浓度的日变化规律,以及与气象因子、PM10、PM_(2.5)相关性。结果表明,水溶性无机离子质量浓度与环境空气中NO_2、CO、PM_(10)、PM_(2. 5)显著相关,与气温、风速、能见度等呈负相关;日变化呈明显单峰型,峰值出现在08:00左右;水溶性无机离子季度均值为27. 2μg/m~3,占ρ(PM_(2.5))平均50%左右,ρ(NO_3~-)、ρ(SO_4~(2-))和ρ(NH_4~+)占ρ(水溶性无机离子)总85%以上;指出,SO_4~(2-)主要受远距离传输的影响,NO_3~-和NH_4~+主要受局地源的影响。     

冬季南京城区气溶胶化学组分和吸湿性观测

大气气溶胶的吸湿特性改变了颗粒物的粒径、光学性质、云凝结核活性,进而对大气能见度、地面辐射强迫和人体健康产生重要影响。针对长三角腹地城市南京重污染天气频发现象,笔者使用吸湿串联电迁移差分分析仪(H-TDMA)结合在线气体组分及气溶胶监测系统(MARGA)和相关气象数据对冬季南京城区气溶胶吸湿增长特性进行外场观测研究。结果表明:灰霾期间SO_4~(2-)、NO_3~-、NH_4~+的质量浓度分别为(17.57±9.07)(26.16±11.39)(13.61±6.68)μg/m~3,非灰霾期间为(9.62±3.58)(12.12±7.51)(5.78±3.59)μg/m~3,前者是后者的2倍。水溶性组分质量浓度大小依次为NO_3~-SO_4~(2-)NH_4~+Cl~-K~+Ca~(2+)Na~+Mg~(2+)。其中NO_3~-的贡献最大,占PM_(2.5)的29%,其次是SO_4~(2-)占14%,NH_4~+占8%。其他水溶性组分(Cl~-、K~+、Ca~(2+)、Na~+、Mg~(2+))约占PM_(2.5)的5.9%。SO_4~(2-)、NO_3~-、NH_4~+的质量浓度没有明显日变化且保持在较高水平。观测期间气溶胶的不同粒径段粒子吸湿增长因子概率密度分布(GF-PDF)均呈双峰,随粒径增大,强吸湿组粒子的吸湿性增大,而弱吸湿组的吸湿性减弱。其中,40 nm粒径段粒子强、弱吸湿增长因子分别为1.335±0.03和1.054±0.008,80 nm粒径段为1.348±0.03和1.053±0.011,40 nm较80 nm粒径段的粒子弱吸湿峰更为明显。灰霾期间粒子的吸湿增长因子分别为1.307±0.08和1.413±0.07,非灰霾期间为1.230±0.03和1.300±0.03。冷锋过境时气溶胶弱吸湿组谱分布没有明显的变化,强吸湿组谱分布明显向弱吸湿方向偏移,吸湿性减弱。灰霾期间和整个观测期间PM_(2.5)的平均质量浓度分别为(87.56±25.87)(69.31±28.75)μg/m~3,灰霾期间主要的二次气溶胶质量浓度占PM_(2.5)的66%,而粒子的平均吸湿增长因子从1.325±0.03降低到1.301±0.07。特殊时段春节期间弱吸湿组粒子的吸湿性增大,而强吸湿组粒子的吸湿性减弱。其中110 nm粒径段粒子强吸湿组吸湿增长因子明显下降,SO_4~(2-)、NO_3~-、NH_4~+的质量浓度也发生明显下降,吸湿增长因子和水溶性化学组分的变化呈良好一致性。     

Five-year monitoring study of chemical characteristics of Wet atmospheric precipitation in the southern region of Jordan

Wet atmospheric samples were collected from different locations in the southern region of Jordan during a 5-year period (October 2006 to May 2011). All samples were analyzed for pH, EC, major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃ ^?, Cl^?, NO₃ ^?, and SO₄ ^2?), and trace metals (Fe²⁺, Al³⁺,Cu²⁺, Pb²⁺, and Zn²⁺). The highest ion concentrations were observed during the beginning of the rainfall events because large amounts of dust accumulated in the atmosphere during dry periods and were scavenged by rain. The rainwater in the study area is characterized by low salinity and neutral pH. The major ions found in rainwater followed the order of HCO₃?>?Cl^??>?SO₄ ^2? and Ca²⁺?>?Na⁺ > Mg²⁺ > NH₄ ⁺ > K⁺. Trace metals were identified to be of anthropogenic origin resulting from cement and phosphate mining activities located within the investigated area and from heating activities during the cold period of the year (January to April). The wet precipitation chemistry was analyzed using factor component analysis for possible sources of the measured species. Factor analysis (principal component analysis) was used to assess the relationships between the concentrations of the studied ions and their sources. Factor 1 represents the contribution of ions from local anthropogenic activities, factor 2 represents the contribution of ions from natural sources, and factor 3 suggests biomass burning and anthropogenic source. Overall, the results revealed that rainwater chemistry is strongly influenced by local anthropogenic sources rather than natural and marine sources, which is in a good agreement with the results obtained by other studies conducted in similar sites around the world.     

大同市大气颗粒物浓度与水溶性离子季度分布特征

为研究大同市大气颗粒物质量浓度与水溶性离子组成特征,于2013年2、7、9、12月,分别对大同市及其对照点庞泉沟国家大气背景点进行了PM2.5及PM10的采样,通过超声萃取-IC法测定了样品中的9种水溶性离子,结果表明,大同市大气颗粒物污染1、4季度重于2、3季度,PM2.5季度均值全年均未超标,PM10仅第1季度超标1.4倍,污染状况总体良好,PM2.5与PM10相关系数R为0.75,说明大同市颗粒物污染有较为相近的来源,且不同季节均以粗颗粒物为主;大同市PM2.5中水溶性离子浓度分布为SO2-4、NO-3、NH+4Cl-、Ca2+K+、Na+F-、Mg2+,PM10中Ca2+浓度仅次于SO2-4、NO-3,控制扬尘将有效降低PM10的浓度;PM2.5及PM10中的9种水溶性离子在不同季度的浓度与颗粒物浓度分布规律类似,1、4季度较高,2、3季度较低;由阴阳离子平衡计算结果可知,相关性方程的斜率K为1.045,表明大同市大气颗粒物中阳离子相对亏损,大气细粒子组分偏酸性。NO-3与SO2-4浓度比值均小于1,大同市以硫酸型污染为主,大气中的SO2-4主要来源于人类活动排放。     

1997—2010年北京市大气降水离子特征变化趋势研究

依据北京市环境保护监测中心1997—2010年降水监测资料,分析北京地区降水中离子特征及变化趋势,阐明北京市降水污染现状及变化特征。结合北京市特有的气象条件、地形地貌和工业分布情况,分析污染物来源及污染变化趋势。研究表明:年度降水电导率呈现波动变化,降水污染严重程度依次为南部郊区>市区>北部背景点。北京地区大气降水中的主要阳离子成分是Ca²⁺和NH₄⁺,主要阴离子成分是SO₄^2-和NO₃^-。近年来[SO₄^2-]/[NO₃^-]比值逐步下降,污染类型由典型硫酸型发展为硫酸+硝酸混合型。阳离子[Ca²⁺]/[NH₄⁺]比值下降,碱性离子缓冲能力降低。9种离子各季节浓度变化趋势基本一致,由高到低依次是春季>秋季>冬季>夏季,这种季节变化特征与气象因素密切相关。相关性及聚类分析表明:NO₃^-与SO₄^2-存在很强相关性,说明其前体物SO₂和NO_X在大气中经常一同排放且进入降水途径相同;H⁺浓度不是由某个离子决定,是所有致酸离子和中和离子相互作用的结果,而NH₄⁺来源不同于其它离子,北京地区的氨存在其单独排放源。     

Chemistry of fogs at Agra, India: Influence of soil particulates and atmospheric gases

Fog water samples were collected in the months of December and January during 1998–2000 at Agra, India. The samples were analyzed for pH, major anions (F⁻, Cl⁻, SO₄ ²⁻, NO₃ ⁻, HCOO⁻ and CH₃COO⁻), major cations (Ca²⁺, Mg²⁺, Na⁺ and K⁺) and NH₄ ⁺ using ion chromatography, ICP-AES and spectrophotometer methods, respectively. pH of fog water samples ranged between 7.0 and 7.6 with a volume weighted mean of 7.2, indicating its alkaline characteristic. NH₄ ⁺ contributed 40%, SO₄ ²⁻ and NO₃ ⁻ accounted for 28%, while Ca²⁺, Mg²⁺, Na⁺ and K⁺ accounted for 16% of the total ionic concentration. The ratios of Mg²⁺/Ca²⁺ and Na⁺/Ca²⁺ in fog water indicates that 50–75% of fog water samples correspond to the respective ratios in local soil. Significant correlation between Ca²⁺, Mg²⁺, Na⁺ and K⁺ suggests their soil origin. The order of neutralization, NH₄ ⁺ (1.4) > Ca²⁺ (0.28) > Mg²⁺ (0.12), indicates that NH₄ ⁺ is the major neutralizing species. Fog water and atmospheric alkalinity were also computed and were found to be 873 and 903 neqm⁻³, respectively. Both of these values are higher than values reported from temperate sites and thus indicate that at the present level of pollutants, there is no risk of acid fog problem. The study also shows that the alkaline nature of fog water is due to dissolution of ammonia gas and partly due to interaction of fog water with soil derived aerosols.     

Atmospheric deposition of major and trace elements in Amman, Jordan

Wet and dry deposition samples were collected in the capital of Jordan, Amman. Concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, V, Zn, Fe, Sr, Mg²⁺, Ca²⁺, Na⁺, K⁺, Cl⁻, NO₃ ⁻ and SO₄ ²⁻, along with pH were determined in collected samples. Mean trace metal concentrations were similar or less than those reported for other urban regions worldwide, while concentrations of Ca²⁺ and SO₄ ²⁻ were among the highest. High Ca²⁺ concentrations were attributed to the calcareous nature of the local soil and to the influence of the Saharan dust. However, high SO₄ ²⁻ concentrations were attributed to the influence of both anthropogenic and natural sources. Except for Cl⁻, NO₃ ⁻, SO₄ ²⁻ and Cu, monthly dry deposition fluxes of all measured species were higher than wet deposition fluxes. The annual wet deposition fluxes of trace metals were much lower than those reported for other urban areas worldwide.     

Occurrence of Acid Rain over Delhi

Precipitation samples were collected as wet-fall only andprimarily on event basis in Delhi during the monsoon period of1995. Concentrations of major anions (SO₄ ^2-,NO₃ ^- and Cl^-) andcations (Ca²⁺, Mg²⁺,Na⁺ and K⁺) were determined. The pH of the rain waterwas found to be more than 5.6, showing alkalinity during theearly phase of monsoon, but during the late phase of monsoon pHtendency was towards acidity due to lack of proper neutralizationof acidic ions. Neutralization is not only due to the localprocess but also due to the pre-monsoon Andhi which bringsSuspended Particulate Matter (SPM) containing Ca²⁺,Mg²⁺, Na⁺ and K⁺ as well as the local emission ofNH₃. In the late monsoon the concentration of cations getsreduced because of heavy rainfall and relatively unfavourablecondition for their transport from the adjoining areas, whereasthe anion concentrations remain unchanged owing to theircontinuous emission.     

Factors and sources influencing ionic composition of atmospheric condensate during winter season in lower troposphere over Delhi, India

Atmospheric condensate (AC) and rainwater samples were collected during 2010–2011 winter season from Delhi and characterized for major cations and anions. The observed order of abundance of cations and anions in AC samples was NH ₄ ⁺ ?>?Ca²⁺?>?Na⁺?>?K⁺?>?Mg²⁺ and HCO ₃ ^? ?>?SO ₄ ^2? ?>?Cl^??>?NO ₂ ^? ?>?NO ₃ ^? ?>?F^?, respectively. All samples were alkaline in nature and Σ _cation/Σ _anion ratio was found to be close to one. NH ₄ ⁺ emissions followed by Ca²⁺ and Mg²⁺ were largely responsible for neutralization of acidity caused by high NO _x and SO₂ emissions from vehicles and thermal power plants in the region. Interestingly, AC samples show low nitrate content compared with its precursor nitrite, which is commonly reversed in case of rainwater. It could be due to (1) slow light-mediated oxidation of HONO; (2) larger emission of NO₂ and temperature inversion conditions entrapping them; and (3) formation and dissociation of ammonium nitrite, which seems to be possible as both carry close correlation in our data set. Principal component analysis indicated three factors (marine mixed with biomass burning, anthropogenic and terrestrial, and carbonates) for all ionic species. Significantly higher sulfate/nitrate ratio indicates greater anthropogenic contributions in AC samples compared with rainwater. Compared with rainwater, AC samples show higher abundance of all ionic species except SO₄, NO₃, and Ca suggesting inclusion of these ions by wash out process during rain events. Ionic composition and related variations in AC and rainwater samples indicate that two represent different processes in time and space coordinates. AC represents the near-surface interaction whereas rainwater chemistry is indicative of regional patterns. AC could be a suitable way to understand atmospheric water interactions with gas and solid particle species in the lower atmosphere.